A Blockchain-Enabled AI-Driven Secure Searchable Encryption Framework for Medical IoT Systems
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a Binary Spring Search technique with AI to secure Internet of Medical Things (IoMT) data on blockchain. The novel approach enhances data searchability and trustworthiness for patient health records.
Area Of Science
- Computer Science
- Cybersecurity
- Health Informatics
Background
- Internet of Medical Things (IoMT) systems utilize blockchain for data storage, but face challenges in data searching, privacy, and security.
- Existing IoMT blockchain solutions require enhanced security and efficient data retrieval mechanisms to ensure patient data integrity and trustworthiness.
Purpose Of The Study
- To propose a novel Binary Spring Search (BSS) technique integrated with a hybrid deep neural network for secure and trustworthy IoMT data management.
- To enhance the security, privacy, and searchability of medical data stored on blockchain within IoMT environments.
- To develop a patient-centered data access model ensuring secure, efficient, and decentralized management of Patient Health Records (PHR).
Main Methods
- Developed a Binary Spring Search (BSS) technique based on group theory, combined with a hybrid deep neural network.
- Integrated blockchain for immutable and decentralized data management, AI for data analysis and threat detection, and searchable encryption for secure queries.
- Implemented secure key revocation and dynamic policy updates within the proposed framework.
- Utilized Hyperledger Fabric (OrigionLab) for blockchain simulations.
Main Results
- The proposed framework significantly reduces transaction times while maintaining high security levels for IoMT data.
- Demonstrated enhanced data searchability and trustworthiness compared to existing methods.
- The blockchain-based architecture ensures integrity and tamper-proof storage of medical data.
Conclusions
- The novel BSS technique integrated with AI and blockchain offers a secure, efficient, and searchable solution for managing Patient Health Records (PHR).
- The patient-centered model enhances data security and demonstrates a positive return on investment for healthcare systems.
- The proposed framework addresses critical privacy and security concerns in IoMT data searching and storage.
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